Walrus has been pondering a seemingly simple yet rarely addressed question since its inception: If the core data of a decentralized system cannot be recovered after a few years, does this system still deserve the label "trustworthy"?

This question hits hard and is precisely what most projects tend to avoid.

In the current Web3 ecosystem, there is an interesting asymmetry. Contract code is strictly guarded and immutable; every transaction leaves a permanent record and is fully traceable. But the ways in which truly valuable content—images, text, AI model parameters, game items, social histories—are stored are alarmingly loose. They all rely on external storage systems, and if these systems fail, the on-chain pointers become a pile of waste paper. What’s the point of pointers without content?

Walrus aims to fill this gap.

What sets it apart is that it doesn’t focus on "how much can be stored," but on "whether data can still be recovered after many years in a scenario where no single node can be fully trusted." Therefore, you’ll see it emphasizes complex mechanisms like erasure coding, object segmentation, and distributed verification, rather than simple, brute-force multi-replica stacking.

From an engineering perspective, anyone can implement multi-replica solutions. They are cheap, straightforward, and easy to calculate costs for. The problem lies in their linear scalability—more replicas mean exaggerated redundancy and wasted resources. Walrus’s coding scheme theoretically requires less redundant data to achieve similar fault tolerance. For a public network that needs to operate stably over the long term, this is a significant advantage.

Everything has its trade-offs. The system’s complexity increases. Operational difficulty rises. But this might be the price of decentralized storage—if we want true persistence, trustworthiness, and low redundancy, simple solutions may simply be infeasible.